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Business Process Performance Prediction on a Tracked Simulation Model

Business Process Performance Prediction on a Tracked Simulation Model. Andrei Solomon , Marin Litoiu – York University. Agenda. Motivation Proposed Architecture State Prediction Results Conclusions. Motivation. Business processes need to adapt to satisfy service level agreements

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Business Process Performance Prediction on a Tracked Simulation Model

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  1. Business Process Performance Prediction on a TrackedSimulation Model Andrei Solomon , Marin Litoiu– York University

  2. Agenda • Motivation • Proposed Architecture • State Prediction • Results • Conclusions

  3. Motivation • Business processes • need to adapt to satisfy service level agreements • monitor • determine changes • Execute

  4. Motivation • analyzing the data • quantitative evaluation of different change decisions • process optimization • needs forecasted key performance indicators • to asses the effect of changes • limitations of current approach: • forecasts based on simple interpolation  inaccurate predictions and wrong decisions • Benefitsfeedback based evolution • architecture that • business agility • more accurate simulation • more accurate predictions • more accurate decisions

  5. States and KPI States: • Raw monitoring metrics • Individual task durations • Message length and frequency • Number of users, etc.. KPIs: • Example: Average Process Duration KPI • KPI definition - specifies the method of calculation, given: • current instances • aggregated metrics • predefined set of aggregation functions (i.e. average) • time period for data collection (example: rolling 30 days = 30 days sliding window) • specifies a desired target • Are defined in Modeling phase

  6. Predictive Feedback Loop • Goal • maintain KPIs close to the reference target • predict short term change • to enable more effective planning and strategic decisions • using estimated states

  7. Case Study (Credit Approval) IBM WebSphere Integration Developer (WID) IBM WebSphere Process Server + Monitor IBM WebSphere Business Modeller Estimation, prediction and integration: our contribution

  8. State Prediction – Linear Regression

  9. State Prediction - ARIMA

  10. Without Simulation

  11. KPI Prediction - Results • (b) Err = 21% • (c) Err = 7% • (a) Err = 23%

  12. Conclusions & Future work Conclusions: • feedback based evolution architecture • automated live monitoring • a KPI prediction module • Forecasts the states (linear regression and ARIMA) • Uses a simulator to correlates the states Further work and future challenges include: • validation - other estimators • modeling human resources • implement an optimizationalgorithm

  13. Thank you. Questions?

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